An image-based decision system processes and extracts information from an image or multiple images to make decisions such as the presence of objects of interest, disease, defects; or the acceptance of measurement parameters such as dimensions, intensity, structures, color, relative position and motion, etc. Image-based decision systems have broad applications such as machine vision, non-contact gauging, inspection, robot guidance, medical imaging, etc.
Many image-based decision tasks involve the detection of defects or gauging of dimensions from man-made manufacturing components, parts or systems. Template matching, golden template comparison and caliper based edge detection are the primary prior art approaches for performing simple machine vision inspection and measurement tasks (Silver, B, “Geometric Pattern Matching for General-Purpose Inspection in Industrial Machine Vision”, Intelligent Vision '99 Conference—Jun. 28–29, 1999).
There is often a teaching phase and an application phase for an image-based decision system. In the prior art approach, template region(s) or a golden template is selected by a human and stored in the system in the teaching phase. In addition, edge detection calipers are specified at image regions of interest for edge detection through multiple one-dimensional projection and simple differentiation. In the application phase, a template search is applied to locate the template region(s) in the input image. The located template locations are used to establish a reference coordinate system and/or for deriving points and structures for measurements. Edges are detected from each caliper regions and/or a golden template is subtracted from the normalized input image for defect detection or dimensional measurements (Hanks, J, “Basic Functions Ease Entry Into Machine Vision”, Test & Measurement World, Mar. 1, 2000; Titus, J, “Software makes machine vision easier”, Test & Measurement World, Oct. 15, 2001)
There are many long-standing difficulties with the prior-art approach.                1. The prior art teaching process requires human selection of template region(s) and the selection and specification of edge calipers and thresholds for measurements. It is mostly performed in an ad-hoc fashion that often requires experienced personnel and an extensive trial-and-error process. The effectiveness of the resulting inspection process is highly dependent on the experience level of the person who sets up the process. This is inconsistent, expensive, and manual (i.e. not automatic).        2. The prior art approach is intended for simple inspection tasks that check simple measurement results against a pre-defined tolerance for a Pass/Fail decision. The measurements are designed according to the convenience of the machine vision system. They often lack physical meaning and do not usually smoothly correlate to physical changes. Therefore, they may only be able to detect gross defects and will have difficulty discriminating between subtle changes and natural variations. For example, a hole measurement algorithm may incorrectly reject a hole due to tilt of the object (for example, caused by a component staging variation) being measured even though the hole is correctly manufactured. Algorithms could be developed to check the tilt angle and other factors before making the hole measurement. However, there may be cases that an incorrectly manufactured hole is mistakenly accepted as a good hole in a tilted object even though the object is not tilted. The creation of a robust algorithm that compensates for different factors requires advanced knowledge, experience and the resulting algorithm could be rather complicated and difficult to test. Some prior-art approaches provide pre-packaged solutions for high volume and well-defined applications to ease this difficulty. However, the pre-packaged approach lacks the flexibility to optimize and adjust configuration and process for many applications.        3. In objects with many components, there is no systematic way of separating variation effects of each component. Therefore, in order to accommodate components with large variations, defects in components with small variations may be missed. Conversely, in order to detect defects in components with small variations, false alarms may be detected from components with large variations.        4. In objects with many components, there is no systematic way of separating effects of each component. Therefore, a defective component in an object may hinder the ability of the inspection system to properly inspect the other components of the object that may also be defective.        5. There is no systematic way of linking structure constraints of components of a common object and check and resolve their consistency. For example, a line on component 1 is designed to be parallel to a line on component 2. When a 10 degree rotation is detected in line component 1, line component 2 is assumed to be rotated by 10 degrees as well. If the measured rotation angle of line component 2 does not match that of line component 1, a method of conflict resolution and estimate refinement should be provided. This is not included in the prior art framework. Special application dependent ad-hoc approach is sometimes used in prior art if the structure linkage is desirable.        6. There is a wealth of design and structure information that are inherently available in manufactured parts. As the quality and precision requirement in manufacturing becomes increasingly important, inspection data becomes a critical resource for production process control. The prior art inspection process only acquires the specific data that are explicity taught into the inspection system. These data are often rather limited. Furthermore, the acquired data are stored in an ad-hoc fashion. There is no standard format and structure of the data. This shortcoming significantly limits the usefulness of machine vision data for statistical process control, process trend and failure analysis and increases the difficulty for users.        7. Prior art approaches cannot be easily generalized to three-dimensional inspection with flexible viewing distance and angles such as parts without fixtures.        